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We'll discuss Generative Networks, as well as the method of Variational Autoencoder Once you've done this, then a sample from a Gaussian distribution is enough for you to create something that looks like it's in the latent space. It has this second term, which is a KL divergence between q theta z given x and p_ phi z. So, it's is a very long process. This is unsupervised in a sense that the final loss is some version of reconstruction error. But that can be too over fitting. It was more a case of reconstruction from original data. This week you will explore Variational AutoEncoders (VAEs) to generate entirely new data. Starting with looking at building regular autoencoder architectures. We'll discuss Generative Networks, as well as the method of Variational Autoencoder And so that will be used as input to the neural network to the VAE model and that's how we represent the input. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. VAE has many different house care application including molecule generations and medical imaging analysis. This week you will explore Variational AutoEncoders (VAEs) to generate entirely new data. That's a vector at z. Then, we can sample from that distribution, that goes with the mean and variance to get the vector z. In this week's assignment, you will generate anime faces and . First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. This week you will explore Variational AutoEncoders (VAEs) to generate entirely new data. We'll call these the mean encoding and the standard deviation of the encoding. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders. This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Variational autoencoders - Unsupervised Approaches in Deep Learning The first step will still be to pass through a network with some bottleneck, so reducing the number of nodes as we did with the regular autoencoders. And on both data Center for QM9 you have the similar kind of scores. Course 3 of 3 in the TensorFlow 2 for Deep Learning Specialization. Thus, as we briefly mentioned in the introduction of this post, a variational autoencoder can be defined as being an autoencoder whose training is regularised to avoid overfitting and ensure that the latent space has good properties that enable generative process. So let's make a start. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. So this process started with drug discovery, and which identify the target to treat, usually a protein. That's autoencoder which we have talked about. Welcome to week 4 - Variational autoencoders - ko.coursera.org The way to get there is recognizing this equality. Deep Learning, Artificial Neural Network, Machine Learning, Reinforcement Learning, keras. And we'll do this with a more complex latent representation of the data. Decoder - Week 3: Variational AutoEncoders | Coursera For any generative model, you're trying to learn the joint distribution. It also has some special character indicating some structures like ring. Explore Bachelors & Masters degrees, Advance your career with graduate-level learning, Generative Adversarial Network (GAN) - Method, Generative Adversarial Network (GAN) - Application, Variational Autoencoder (VAE) - Application. This integral is expensive because you have to integrate all the values of z, so that's expensive, difficult to compute. Those generated molecule they can check their properties and hopefully there are similar to the ones in the database. We'll discuss Generative Networks, as well as the method of Variational Autoencoder The parameters for that normal distribution will be learned by the encoder portion of our network within this variational autoencoder, and then fed through to our learned decoder portion to produce the images. And here each row has some kind of a method or the original data. Variational autoencoders are often associated with the autoencoder model . We will try to cover that in the next few slides to give you a flavor why VAE is a solid model. In the VAE algorithm two networks are jointly learned: an encoder or inference network, as well as a decoder or generative network. Video created by - for the course "Advanced Deep Learning Methods for Healthcare". b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. This posterior probability for some arbitrary encoder or arbitrary distribution is difficult to calculate. Maybe we can approximate this p Phi z given x with another distribution, q Theta z given x so that this q Theta is actually a simpler distribution. To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics. Instead of having an Input x, you have Input x and also another input epsilon. But now at step two, we are going to be learning a mu and a sigma for each value that are meant to represent a normal distribution from which values can be samples. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications Autoencoders - Part 1 6:51 After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Video created by University of Illinois at Urbana-Champaign for the course "Advanced Deep Learning Methods for Healthcare". We'll also generate noise using a Gaussian distribution so that our actual encoding will be sampled from the Gaussian distribution using our standard deviation and our mean by multiplying out the noise by the standard deviation and then adding the results to the mean. This autoencoder oftentimes, this latent code h is very sensitive. This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. In particular, it is assumed that you are familiar with standard probability distributions, probability density functions, and concepts such as maximum likelihood estimation, change of variables formula for random variables, and the evidence lower bound (ELBO) used in variational inference. The problem is, if you does this sampling during the training phase, the gridding would not be able to pass back, when you introduce this sampling process. Variational Autoencoder (VAE) - Application - Week 4 - Generative We are looking for molecule with certain property, right? We will cover autoencoders and GAN as examples. For a given data point x, the loss has this two term, the first term as the negative log-likelihood of p_ phi x given z. In VAEs case, it's a Gaussian distribution. And it can then be combined with the learned values from the encoder to emulate what the training data would look like, so that the output layers can reconstruct. But also the loss function is different, which we'll talk about. That's one step differences. The idea of VAE is we still want to do this encoder and decoder strategy in unsupervised way. Video created by Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign for the course "Advanced Deep Learning Methods for Healthcare". For the generation process, molecule generation using VAE, what they have done is the input is SMILE string as sequence. Next, we talk about an application of VAE for drug discovery. Were a learned representation of the input data is encoded this could then pass through a decoder to give a reconstruction of the input. Variational autoencoders are one of the most popular types of likelihood-based generative deep learning models. If you are familiar with base rule you will see that this posterior probability of p z given x is p of x given z times pz and divided by p of x. That's our objective. Variational Autoencoder (VAE) - Method - Week 4 - Generative Models This is the evidence lower bound or ELBO, which we also used when we looked at the Bayes by Backprop algorithm earlier in the course. The generative process can be written as follows. Variational Autoencoder (VAE) - Method - Week 4 - Generative Models And so in, I mean, in this context we're going to give you an example for the drug discovery phase, that is, identify some promising molecules. We also have some prior distribution, a p_phi z, where that's on the latent embedding z. And follow the atoms and so just turn this molecule, which is a graph structure, into a line or into a string. Video created by for the course "Advanced Deep Learning Methods for Healthcare". And then they can go through this decoding process to get, potentially, a new SMILES string or new molecules. VAE want to fix that. You can use a different loss function where there's a squared Euclidean loss or some cross-entropy loss. Video created by for the course "Advanced Deep Learning Methods for Healthcare". Z is just mu_ x plus this is sigma to the square root power effects and times Epsilon. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. Variational autoencoder. But there's a problem. Variational Autoencoder (VAE) - Method - Week 4 - Coursera So little details, want to cover the data. You will not see a good structure in the latent space. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. You will then use the trained networks to encode data examples into a compressed latent space, as well as generate new samples from the prior distribution and the decoder. Video created by University of Illinois at Urbana-Champaign for the course "Advanced Deep Learning Methods for Healthcare". In this week you will learn how to implement the VAE using the TensorFlow Probability library. a) Learn neural style transfer using transfer learning: extract the content of an image (eg. In the previous weeks of the course, you've seen how to develop different types of probabilistic deep learning models using the TensorFlow probability library. We will cover autoencoders and GAN as examples. And the idea is actually quite simple, the goal is, if we have seen many many molecules that's in our database, we know their properties, can we learn some generative model to generate new molecules with similar properties? After that we get into the drug development phase, and there the overall goal of drug development is to determine whether some of the hits are safe and effective for treating the disease. Video created by Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign for the course "Advanced Deep Learning Methods for Healthcare". We'll discuss Generative Networks, as well as the method of Variational Autoencoder This course follows on from the previous two courses in the specialisation, Getting Started with TensorFlow 2 and Customising Your Models with TensorFlow 2.
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